A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging.

PURPOSE: To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians. METHODS: A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance. RESULTS: A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise. CONCLUSIONS: This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses. LEVEL OF EVIDENCE: Level III, retrospective comparative case series.

A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood.

Background: Autism spectrum disorder (ASD) is associated with altered brain development, but it is unclear which specific structural changes may serve as potential diagnostic markers, particularly in young children at the age when symptoms become fully established. Furthermore, such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level. Objective: This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children. Methods: A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) together with symptom severity and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets. Results: Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development. The majority (29/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification achieved very high accuracy of 96.77% in the discovery dataset, and 91.67% and 88.89% in the two independent validation datasets. Conclusions: Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.

BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning.

Objectives: To propose a deep learning-based classification framework, which can carry out patient-level benign and malignant tumors classification according to the patient's multi-plane images and clinical information. Methods: A total of 430 cases of spinal tumor, including axial and sagittal plane images by MRI, of which 297 cases for training (14072 images), and 133 cases for testing (6161 images) were included. Based on the bipartite graph and attention learning, this study proposed a multi-plane attention learning framework, BgNet, for benign and malignant tumor diagnosis. In a bipartite graph structure, the tumor area in each plane is used as the vertex of the graph, and the matching between different planes is used as the edge of the graph. The tumor areas from different plane images are spliced at the input layer. And based on the convolutional neural network ResNet and visual attention learning model Swin-Transformer, this study proposed a feature fusion model named ResNetST for combining both global and local information to extract the correlation features of multiple planes. The proposed BgNet consists of five modules including a multi-plane fusion module based on the bipartite graph, input layer fusion module, feature layer fusion module, decision layer fusion module, and output module. These modules are respectively used for multi-level fusion of patient multi-plane image data to realize the comprehensive diagnosis of benign and malignant tumors at the patient level. Results: The accuracy (ACC: 79.7%) of the proposed BgNet with multi-plane was higher than that with a single plane, and higher than or equal to the four doctors' ACC (D1: 70.7%, p=0.219; D2: 54.1%, p<0.005; D3: 79.7%, p=0.006; D4: 72.9%, p=0.178). Moreover, the diagnostic accuracy and speed of doctors can be further improved with the aid of BgNet, the ACC of D1, D2, D3, and D4 improved by 4.5%, 21.8%, 0.8%, and 3.8%, respectively. Conclusions: The proposed deep learning framework BgNet can classify benign and malignant tumors effectively, and can help doctors improve their diagnostic efficiency and accuracy. The code is available at https://github.com/research-med/BgNet.

Application of Magnetic Resonance Imaging in Neoadjuvant Treatment of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignant tumors of the human digestive system. Due to its insidious onset, many patients have already lost the opportunity for radical resection upon tumor diagnosis. In recent years, neoadjuvant treatment for patients with borderline resectable PDAC has been recommended by multiple guidelines to increase the resection rate of radical surgery and improve the postoperative survival. However, further developments are required to accurately assess the tumor response to neoadjuvant therapy and to select the population suitable for such treatment. Reductions in drug toxicity and the number of neoadjuvant cycles are also critical. At present, the clinical evaluation of neoadjuvant treatment is mainly based on several serological and imaging indicators; however, the unique characteristics of PDAC and the insufficient sensitivity and specificity of the markers render this system ineffective. The imaging evaluation system, magnetic resonance imaging (MRI), has its own unique imaging advantages compared with computed tomography (CT) and other imaging examinations. One key advantage is the ability to reflect the changes more rapidly in tumor tissue components, such as the degree of fibrosis, microvessel density, and tissue hypoxia. It can also perform multiparameter quantitative analysis of tumor tissue and changes, attributing to its increasingly important role in imaging evaluation, and potentially the evaluation of neoadjuvant treatment of pancreatic cancer, as several current articles have studied. At the same time, owing to the complexity of MRI and some of its limitations, its wider application is limited. Compared with CT imaging, few relevant studies have been conducted. In this review article, we will investigate and summarize the advantages, limitations, and future development of MRI in the evaluation of neoadjuvant treatment of PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T2*-weighted radiomic-based extra trees models.

OBJECTIVES: Conventional MRI may not be ideal for predicting cervical spondylotic myelopathy (CSM) prognosis. In this study, we used radiomics in predicting postoperative recovery in CSM. We aimed to develop and validate radiomic feature-based extra trees models. METHODS: There were 151 patients with CSM who underwent preoperative T2-/ T2*-weighted imaging (WI) and surgery. They were divided into good/poor outcome groups based on the recovery rate. Datasets from multiple scanners were randomised into training and internal validation sets, while the dataset from an independent scanner was used for external validation. Radiomic features were extracted from the transverse spinal cord at the maximum compressed level. Threshold selection algorithm, collinearity removal, and tree-based feature selection were applied sequentially in the training set to obtain the optimal radiomic features. The classification of intramedullary increased signal on T2/T2*WI and compression ratio of the spinal cord on T2*WI were selected as the conventional MRI features. Clinical features were age, preoperative mJOA, and symptom duration. Four models were constructed: radiological, radiomic, clinical-radiological, and clinical-radiomic. An AUC significantly > 0.5 was considered meaningful predictive performance based on the DeLong test. The mean decrease in impurity was used to measure feature importance. p < 0.05 was considered statistically significant. RESULTS: On internal and external validations, AUCs of the radiomic and clinical-radiomic models, and radiological and clinical-radiological models ranged from 0.71 to 0.81 (significantly > 0.5) and 0.40 to 0.55, respectively. Wavelet-LL first-order variance was the most important feature in the radiomic model. CONCLUSION: Radiomic features, especially wavelet-LL first-order variance, contribute to meaningful predictive models for CSM prognosis. KEY POINTS: • Conventional MRI features may not be ideal in predicting prognosis. • Radiomics provides greater predictive efficiency in the recovery from cervical spondylotic myelopathy.

Utility of Advanced DWI in the Detection of Spinal Cord Microstructural Alterations and Assessment of Neurologic Function in Cervical Spondylotic Myelopathy Patients.

BACKGROUND: Diffusion-weighted imaging (DWI) can quantify the microstructural changes in the spinal cord. It might be a substitute for T2 increased signal intensity (ISI) for cervical spondylotic myelopathy (CSM) evaluation and prognosis. PURPOSE: The purpose of the study is to investigate the relationship between DWI metrics and neurologic function of patients with CSM. STUDY TYPE: Retrospective. POPULATION: Forty-eight patients with CSM (18.8% females) and 36 healthy controls (HCs, 25.0% females). FIELD STRENGTH/SEQUENCE: 3 T; spin-echo echo-planar imaging-DWI; turbo spin-echo T1/T2; multi-echo gradient echo T2*. ASSESSMENT: For patients, conventional MRI indicators (presence and grades of T2 ISI), DWI indicators (neurite orientation dispersion and density imaging [NODDI]-derived isotropic volume fraction [ISOVF], intracellular volume fraction, and orientation dispersion index [ODI], diffusion tensor imaging [DTI]-derived fractional anisotropy [FA] and mean diffusivity [MD], and diffusion kurtosis imaging [DKI]-derived FA, MD, and mean kurtosis), clinical conditions, and modified Japanese Orthopaedic Association (mJOA) were recorded before the surgery. Neurologic function improvement was measured by the 3-month follow-up recovery rate (RR). For HCs, DWI, and mJOA were measured as baseline comparison. STATISTICAL TESTS: Continuous (categorical) variables were compared between patients and HCs using Student's t-tests or Mann-Whitney U tests (chi-square or Fisher exact tests). The relationships between DWI metrics/conventional MRI findings, and the pre-operative mJOA/RR were assessed using correlation and multivariate analysis. P < 0.05 was considered statistically significant. RESULTS: Among patients, grades of T2 ISI were not correlated with pre-surgical mJOA/RR (P = 0.717  and 0.175, respectively). NODDI ODI correlated with pre-operative mJOA (r = -0.31). DTI FA, DKI FA, and NODDI ISOVF were correlated with the recovery rate (r = 0.31, 0.41, and -0.34, respectively). In multivariate analysis, NODDI ODI (DTI FA, DKI FA, NODDI ISOVF) significantly contributed to the pre-operative mJOA (RR) after adjusting for age. DATA CONCLUSION: DTI FA, DKI FA, and NODDI ISOVF are predictors for prognosis in patients with CSM. NODDI ODI can be used to evaluate CSM severity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 5.

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy.

INTRODUCTION: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM. MATERIALS AND METHODS: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent  scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy. RESULTS: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC < .05 and/or P accuracy < .05). One radiological model performed better than random guessing during CV (P accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P AUROC = .048). CONCLUSION: Radiomic features can predict postoperative spinal cord function in CSM patients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.

[Applications of Artificial Intelligence in Musculoskeletal System Imaging].

Artificial intelligence (AI) represents the latest wave of computer revolution and is considered revolutionary technology in many industries including healthcare. AI has been applied in medical imaging mainly due to the improvement of computational learning,big data mining,and innovations of neural network architecture. AI can improve the efficiency and accuracy of imaging diagnosis and reduce medical cost;also,it can be used to predict the disease risk. In this article we summarize and analyze the application of AI in musculoskeletal imaging.

Association of atherosclerotic plaque features with collateral circulation status in elderly patients with chronic carotid stenosis.

OBJECTIVE: To determine the association of carotid plaque features with collateral circulation status in elderly patients with moderate to severe carotid stenosis. METHODS: Elderly patients (> 60 years) with moderate to severe carotid stenosis were recruited and categorized into good and poor collateral circulation groups, and underwent magnetic resonance imaging and computed tomography imaging. The carotid plaque features including lipid-rich necrotic core, intraplaque hemorrhage, calcification, and fibrous cap rupture (FCR) were evaluated, and maximum wall thickness, normalized wall index (NWI), and luminal stenosis were measured. The association between these variables and collateral circulation status was analyzed. RESULTS: Of the 97 patients (78 males, mean age: 69.0 ± 6.1 years), 19 (19.6%) had poor collaterals. The poor collateral group had a significantly higher NWI (93.7% ± 5.0% vs. 89.0% ± 7.9%, P = 0.011), a greater extent of stenosis (80.0% ± 11.4% vs. 75.3% ± 9.4%, P = 0.036) and FCR (84.2% vs. 55.1%, P = 0.020) compared with good collateral group. Carotid NWI (OR = 3.83, 95% CI: 1.36-10.82, P = 0.011) and more FCR (OR = 6.77, 95% CI: 1.35-33.85, P = 0.020) were associated with poor collateral circulation after adjustment for the confounding factors. The combination of NWI, FCR, systolic blood pressure, and triglycerides had the highest area-under-the-curve (AUC = 0.85) for detection of poor collaterals. CONCLUSIONS: Carotid plaque features, specifically NWI and FCR, are independently associated with poor collateral circulation, and the combination of carotid plaque features and traditional risk factors has a stronger predictive value for poor collateral circulation than plaque features alone.

A Prospective Randomized Multicenter Controlled Trial on Salvianolate for Treatment of Unstable Angina Pectoris in A Chinese Elderly Population.

OBJECTIVE: To evaluate the efficacy and safety of salvianolate in elderly patients with unstable angina pectoris (UAP). METHODS: A prospective double-blind randomized placebo-controlled multicenter trial in elderly patients with UAP from 13 third-grade class-A hospitals in China was performed. A total of 318 patients were randomly allocated in a 1:1 ratio to an experimental group (160 patients) and a control group (158 patients). The experimental group was treated with salvianolate for 14 days on the basis of conventional medicine, and the control group was given a placebo for 14 days with the same criteria. Follow-up was lasted 28 days in both groups. The primary endpoint was biweekly frequency of angina pectoris attacks. The secondary endpoints included biweekly dosage of nitroglycerin, the Seattle Angina Questionnaire, angina pectoris severity and duration, myocardial injury markers, high-sensitivity C-reactive protein (hs-CRP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), as well as major adverse cardiovascular events (MACEs). Safety was assessed according to adverse events and serious adverse events. RESULTS: Baseline characteristics were similar between treatment groups. Compared with those in the control group, the frequency of biweekly angina attacks (2.92 vs . 4.08, P=0.025), the biweekly dosage of nitroglycerin, as well as the severity and duration of angina attacks (P<0.01) were reduced by salvianolate. The Seattle Angina Questionnaire score was also significantly improved in the experimental group than in the control group (P<0.05). No significant differences were observed between the two groups with respect to the incidence of MACEs. Salvianolate was well tolerated. CONCLUSIONS: Salvianolate appear to have efficacy and well tolerated for elderly patients with UAP. [ClinicalTrials.gov identifier: NCT03037047].

Intraosseous schwannoma of the mobile spine: a report of twenty cases.

PURPOSE: To clarify the clinical features, surgical strategies, and outcomes of intraosseous schwannoma (IOS) of the mobile spine. METHODS: We retrospectively reviewed patients with primary benign spinal schwannoma who underwent surgery in our orthopedic department. RESULTS: A total of 101 patients with primary benign schwannoma located in the mobile spine underwent surgery in our orthopedic department from 2005 to 2015. Twenty-five patients presented with aggressive features. Twenty patients were regularly followed up, twelve with lesions in the cervical spine, six with lesions in the thoracic region, and two with lesions in the lumbar spine. Preoperative CT-guided biopsy was performed in fourteen cases; the accuracy of diagnosis was 100%, and IOS is not histologically different from conventional schwannoma. The computed tomography (CT) scan revealed expansile and osteolytic bone destruction in all these cases, with six patients having pathological fracture. On T2-weighted magnetic resonance imaging, the lobulated schwannomas showed heterogeneous signal intensity and significant heterogeneous enhancement on post-contrast images. Gross total resection was performed in seventeen patients and subtotal resection in three. Tumor-involved nerve roots resection were documented to decrease local recurrence in fourteen cases. The visual analog scale score decreased from 5.66 ± 1.79 preoperatively to 1.16 ± 1.77 at the final follow-up. No local recurrence was noticed at the final follow-up. CONCLUSION: CT-guided biopsy is effective for the preoperative diagnosis of spinal IOS. Total resection is the optimal treatment for IOS, whereas subtotal resection could be an alternative choice for high-risk cases. These slides can be retrieved under electronic supplementary material.

[Value of Diffusion-weighted Imaging in Evaluating the Activity of Sacroiliitis in Ankylosing Spondylitis].

Objective To investigate the clinical value of diffusion-weighted imaging (DWI) for evaluating the activity of sacroiliitis in ankylosing spondylitis (AS).Methods Totally 73 AS patients were prospectively enrolled and divided into active group (n=43) and chronic group (n=30) according to Bath ankylosing spondylitis disease activity index (BASDAI) scores and laboratory findings. Conventional magnetic resonance imaging (MRI) and DWI were performed in all subjects. Apparent diffusion coefficient (ADC) values of subchondral lesions in sacroiliac joint were independently measured by two radiologists,and the relative ADC (rADC) values were calculated. ADC and rADC values were compared between active and chronic groups. The efficiencies of ADC and rADC values for differentiating the activity of sacroiliitis were analyzed. In addition,the correlation coefficients of ADC values,rADC values,and BASDAI scores were calculated.Results The ADC and rADC values in the active group were (0.667±0.122)×10 -3 mm 2/s and (1.715±0.343)×10 -3 mm 2/s,respectively,which were significantly higher than those of the chronic group [(0.492±0.0651)×10 -3 mm 2/s and (1.289±0.209)×10 -3 mm 2/s,respectively)] (P<0.0001). The agreement of measurement results between two radiologists was good,and all the interclass correlation coefficients were >0.81. The correlation coefficients of ADC value and rADC value with BASDAI scores were 0.82 and 0.80,respectively (P<0.0001). The optimal cutoff values of ADC value and rADC value for differentiating AS activity were 0.545×10 -3 mm 2/s and 1.467×10 -3 mm 2/s,respectively,The specificity was 81.8% for both indicators,and the sensitivity was 92.0% and 88.0%,respectively.Conclusion DWI is helpful in the quantitative assessment of the activity of sacroiliitis in AS patients.

Comparison of FSE T2 W PROPELLER and 3D-FIESTA of 3 T MR for the internal auditory canal.

OBJECTIVE: The study compared the use of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique fast spin echo (FSE) T2 W and the sequence of three-dimensional fast imaging employing steady-state acquisition (3D-FIESTA) technique in the MRI of the internal auditory canal for overall image quality improvement. MATERIALS AND METHODS: One hundred thirty-two patients undergoing FSE T2 W PROPELLER and 3D-FIESTA examinations of the internal auditory canal were included. All examinations were performed at 3.0 T with comparison of a sagittal oblique FSE T2 W sequence with the PROPELLER technique to 3D-FIESTA in the same reconstructed orientation with PROPELLER. Image quality was evaluated by two radiologists using a 4-point scale. The Wilcoxon signed rank test was used to compare the data of the two techniques. RESULTS: The image quality of FSE T2 W PROPELLER was significantly improved compared to the reconstructed images of 3D-FIESTA. Observer 1: median FSE T2 W with PROPELLER, 4 [mean, 3.455] versus median reconstructed 3D-FIESTA, 3 [mean, 3.15], (P<.001); Observer 2: median FSE T2 W with PROPELLER, 4 [mean, 3.47] versus median reconstructed 3D-FIESTA, 3 [mean, 3.25], (P<.001). Interobserver agreement was good (k value, 0.73) for the rating of the overall image quality. CONCLUSION: The FSE T2 W PROPELLER technique for MRI of internal auditory canal reduced uncertainty caused by motion artifact and improved the quality of the image compared to the reconstructed 3D-FIESTA. It was affected by different parameters including the blade width, echo train length (ETL). This is explained by data oversampling at the center region of k-space, which requires additional imaging time over conventional MRI techniques. Increasing blade was expected to improve motion correction effects but also the signal-to-noise ratio. ETL increases the image sharpness and the overall image quality.

Percutaneous radiofrequency ablation for spinal osteoid osteoma and osteoblastoma.

PURPOSES: We sought to verify the efficacy and safety of RFA in spinal OO and osteoblastomas (OB) (Enneking Stage 2, S2). METHODS: We retrospectively reviewed patients treated in our hospital. Surgical resection was indicated for Enneking Stage 3 OB. RFA indications for spinal OO and OB (S2) were no neurological deficits, complete bone cortex around the lesion on computed tomography (CT), and cerebrospinal fluid between a lesion and the spinal cord/nerve root on magnetic resonance imaging. Abundant cerebrospinal fluid (more than 1.0 mm) between the lesion and nerve root/spinal cord was preferred to prevent neurological damage by heat. Otherwise, surgery was recommended. The minimum follow-up was 24 months. RESULTS: Ten patients were treated with CT-guided percutaneous RFA, including three with OB and seven with OO. No patients had neurological deficits or scoliosis. In OO patients, the average visual analog scale (VAS) scores were 7.6/10 (range 6-10) before RFA. In OB cases, the VAS scores were 8, 7, and 9 before RFA. Nine patients had a one-stage biopsy and then RFA, and one patient had a two-stage procedure (biopsy before RFA). The average RFA time for OO was 10 min (range 4-12). In the three OB cases, the RFA time was 12, 12, and 24 min. The time of the whole produce was 98 min (range 65-130 min). All 10 patients were followed-up. The average follow-up time of OO was 46.6 months (range 24-66). Six patients were free of pain, except one who suffered occasional pain with VAS 2/10. The three OB cases were free of pain at 24, 26, and 26 months. CONCLUSION: CT-guided percutaneous RFA is a safe and effective treatment for spinal OO and S2 OB, especially in lesions with no neurological deficits and intact cortical bone. Cerebrospinal fluid around the lesion is an appropriate indication for percutaneous RFA.

Significance of Diffusion Tensor Imaging of Vastus Medialis Oblique in Recurrent Patellar Dislocation.

BACKGROUND: Numerous studies have investigated the influence of osseous factors on patellofemoral joint instability, but research on the influence of dynamic muscle factors in vivo is still in the exploratory stage. This study aimed to use magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to evaluate vastus medialis oblique (VMO) fiber bundles in patients with recurrent patellar dislocation to explore the changes in muscle morphology and function. METHODS: This prospective study involved 30 patients (7 males and 23 females; average age, 21.4 ± 3.8 years) clinically diagnosed with recurrent patellar dislocation in Peking University Third Hospital and 30 healthy volunteers matched for age, sex, and body mass index in our medical school between January 2014 and October 2014. None of the patients had a recent history of traumatic patellar dislocation or transient patellar dislocation. All patients underwent conventional MRI and DTI of the knee. The cross-sectional area of the VMO on MRI and the fractional anisotropy (FA), apparent diffusion coefficient (ADC), and primary (λ1), secondary (λ2), and three-level characteristic (λ3) values on DTI were measured. The independent-samples t-test was used to compare these parameters between the two groups. RESULTS: Compared with the control group, the patient group showed significantly higher FA values (0.39 ± 0.05 vs. 0.33 ± 0.03) and significantly lower ADC (1.51 ± 0.13 vs. 1.58 ± 0.07), λ2 (4.96 ± 0.13 vs. 5.04 ± 0.07), and λ3 values (4.44 ± 0.14 vs. 4.58 ± 0.07; t = 5.99, t = -2.58, t = -3.02, and t = -4.88, respectively; all P < 0.05). Cross-sectional VMO area and λ1 values did not differ between the two groups (t = -1.82 and t = 0.22, respectively; both P > 0.05). CONCLUSIONS: The functional status of the VMO is closely associated with recurrent patellar dislocation. MRI, especially DTI (FA, ADC, λ2, and λ3), can detect early changes in VMO function and might facilitate the noninvasive monitoring of the functional status of the VMO in patients with recurrent patellar dislocation.

A Volumetric and Functional Connectivity MRI Study of Brain Arginine-Vasopressin Pathways in Autistic Children.

Dysfunction of brain-derived arginine-vasopressin (AVP) systems may be involved in the etiology of autism spectrum disorder (ASD). Certain regions such as the hypothalamus, amygdala, and hippocampus are known to contain either AVP neurons or terminals and may play an important role in regulating complex social behaviors. The present study was designed to investigate the concomitant changes in autistic behaviors, circulating AVP levels, and the structure and functional connectivity (FC) of specific brain regions in autistic children compared with typically developing children (TDC) aged from 3 to 5 years. The results showed: (1) children with ASD had a significantly increased volume in the left amygdala and left hippocampus, and a significantly decreased volume in the bilateral hypothalamus compared to TDC, and these were positively correlated with plasma AVP level. (2) Autistic children had a negative FC between the left amygdala and the bilateral supramarginal gyri compared to TDC. The degree of the negative FC between amygdala and supramarginal gyrus was associated with a higher score on the clinical autism behavior checklist. (3) The degree of negative FC between left amygdala and left supramarginal gyrus was associated with a lowering of the circulating AVP concentration in boys with ASD. (4) Autistic children showed a higher FC between left hippocampus and right subcortical area compared to TDC. (5) The circulating AVP was negatively correlated with the visual and listening response score of the childhood autism rating scale. These results strongly suggest that changes in structure and FC in brain regions containing AVP may be involved in the etiology of autism.

Preoperative Evaluation of the Grade and Stability of Osteochondritis Dissecans of the Knee by Magnetic Resonance Imaging.

Objective To assess the diagnostic performance of magnetic resonance imaging (MRI) in the grading of osteochondritis dissecans (OCD) of the knee.Methods Totally 47 patients with OCD of the knee confirmed by arthroscopy were retrospectively enrolled in this study.The OCD lesions were classified into four stages according to classification system of the International Cartilage Repair Society.Two radiologists analyzed all MRI findings independently,and the results were compared with those of arthroscopy.Sensitivity,specificity,and accuracy were calculated.Kappa value were calculated to quantify inter-observer agreement of the diagnostic OCD grade between two doctors by MRI.Specificity,sensitivity,and accuracy of MRI criteria indicating instability for detection of OCD instability were calculated.Results Of these 47 patients with 48 OCD lesions,stages Ⅰ,Ⅱ,Ⅲ,and Ⅲ lesions were detected in 4,8,16,and 20 patients,respectively.The specificity,sensitivity,and accuracy for the diagnosis of OCD stability were 75.0% (83.3%),88.9% (86.1%),and 85.4% (85.4%) for observer l (2),and the agreement of OCD grade between these two readers was substantial with a Kappa value of 0.82.The specificity,sensitivity,and accuracy of MRI criteria for the detection of OCD instability including high T2 signal intensity at the interface between the OCD and the underlying bone,multiple cysts or a single cyst of>5 mm in diameter surrounding OCD lesions,high T2 signal intensity cartilage fracture line traversing the articular cartilage,and osteochondral defect were 83.3%,80.6%,and 81.3%;75.0%,72.2%,and 72.9%;66.7%,69.4%,and 68.8%;100%,86.1%,and 89.6%,respectively. Conclusions Osteochondral defect is the most specific MRI sign for diagnosing instable OCD of the knee,whereas osteochondral fracture line has the lowest accuracy.MRI is a useful method to evaluate the grade and stability of OCD of the knee.

Computed Tomography and Magnetic Resonance Manifestations of Spinal Pigmented Villonodular Synovitis.

OBJECTIVE: To study computed tomography (CT) and magnetic resonance imaging (MRI) findings of spinal pigmented villonodular synovitis (PVNS), with the aim of improving diagnostic accuracy. METHODS: Findings from CT (n = 7) and MRI (n = 5) of 7 cases of spinal PVNS, diagnosed by pathological examination, were retrospectively analyzed. RESULTS: One case presented with a small lesion, whereas the remaining 6 cases showed lobulated soft tissue masses centering on the facet joints, with lytic expansive destruction of the bone. The CT density of lesions in 3 cases was similar to that of the surrounding muscles, whereas the remaining 4 cases had lesions that were slightly hypodense relative to muscle. Among 5 cases for which MRI was available, 1 lesion was small with a hypointense edge, 1 manifested homogenous intensity without any obvious hypointense nodule, and multiple hypointensity nodules were visible in the remaining cases. CONCLUSIONS: The CT and MR manifestations of spinal PVNS have certain characteristics and can provide the basis for the preoperative diagnosis.

[Value of multiple b-value diffusion-weighted imaging for differentiation of benign and malignant pulmonary masses].

OBJECTIVE: To explore the value of multiple b-value diffusion-weighted imaging (DWI) for differentiation of benign and malignant pulmonary masses. METHODS: Thirty-eight patients were examined by routine sequences and DWI pulse sequence. DWI was acquired through a single-shot echo-planar imaging combined with a respiratory-triggered mode and parallel acquisition. Nine b values ranging from 0 to 1500 s/mm(2) (0, 50, 100, 150, 200, 400, 600, 1000, 1500 s/mm(2)) were used. The intravoxel incoherent motion model was applied to estimate pure diffusion coefficient D, perfusion-related diffusion coefficient D(*), and perfusion fraction f. Mann-Whitney U test was used to compare all measured parameters between benign and malignant groups. The diagnostic performance of the related parameters was evaluated with receiver operating characteristics (ROC) analysis. RESULTS: Of these 38 patients, 30 were pathologically confirmed and 8 were diagnosed based on clinical data. There were 23 lung malignant masses and 15 benign lesions. A significant reduction of D was found in malignant group than in benign group (Z=3.308, P=0.001), while no significant differences in D(*)(Z=1.646, P=0.100) and f(Z=1.254, P=0.210) were observed between the two groups. The area under the ROC curve for D value (0.839) was largest. When the cutoff value was selected as 0.90×10(-3) mm(2)/s, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of diagnosing malignant masses were 95.7%, 80.0%, 90.9%, 91.7%, and 88.9%,respectively. CONCLUSION: The D value in multiple b-value DWI has certain significance in differentiating the benign and malignant pulmonary masses and has the best diagnostic efficiency.